Deep learning - Deep Learning for Precision Health

Magnetoencephalography (MEG) is a functional neuroimaging modality that records the magnetic fields induced by neuronal activity. It provides better temporal resolution than fMRI and is less affected by noise from intervening tissues than EEG. We propose a data driven, fully automated approach that extracts statistically independent MEG components and a convolutional neural network to discriminate the artifactual components from neuronal ones, without tedious manual labeling. Our custom, 10-layer Convolutional Neural Network (CNN) directly labels eye-blink artifacts. The spatial features the CNN learns are visualized using attention mapping, to reveal what it has learned and bolster confidence in the method's ability to generalize to unseen data.